# Synthetic Data for the Mitigation of Demographic Biases in Face Recognition arXiv: https://arxiv.org/abs/2402.01472 ยท HTML: https://mlsanity.com/paper/2402.01472/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 40 references that are in the map. - First posted on arXiv 2 Feb 2024. ## Builds on (most cited first, 26 of 26) - [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018) - [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017) - [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016) - [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014) - [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021) - [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018) - [Learning Not to Learn: Training Deep Neural Networks with Biased Data](https://mlsanity.com/paper/1812.10352/) (2018) - [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015) - [Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning](https://mlsanity.com/paper/1911.10692/) (2019) - [DCFace: Synthetic Face Generation with Dual Condition Diffusion Model](https://mlsanity.com/paper/2304.07060/) (2023) - [SynFace: Face Recognition with Synthetic Data](https://mlsanity.com/paper/2108.07960/) (2021) - [DigiFace-1M: 1 Million Digital Face Images for Face Recognition](https://mlsanity.com/paper/2210.02579/) (2022) - [Deep Imbalanced Learning for Face Recognition and Attribute Prediction](https://mlsanity.com/paper/1806.00194/) (2018) - [A Comprehensive Study on Face Recognition Biases Beyond Demographics](https://mlsanity.com/paper/2103.01592/) (2021) - [Face Recognition: Too Bias, or Not Too Bias?](https://mlsanity.com/paper/2002.06483/) (2020) - [SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data](https://mlsanity.com/paper/2206.10520/) (2022) - [GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variations](https://mlsanity.com/paper/2305.19962/) (2023) - [Fairness in Biometrics: a figure of merit to assess biometric verification systems](https://mlsanity.com/paper/2011.02395/) (2020) - [InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics](https://mlsanity.com/paper/2004.06592/) (2020) - [On the use of automatically generated synthetic image datasets for benchmarking face recognition](https://mlsanity.com/paper/2106.04215/) (2021) - [Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics](https://mlsanity.com/paper/1912.01842/) (2019) - [Evaluating Proposed Fairness Models for Face Recognition Algorithms](https://mlsanity.com/paper/2203.05051/) (2022) - [Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No](https://mlsanity.com/paper/1911.06396/) (2019) - [On the Applicability of Synthetic Data for Face Recognition](https://mlsanity.com/paper/2104.02815/) (2021) - [Demographic Fairness in Biometric Systems: What do the Experts say?](https://mlsanity.com/paper/2105.14844/) (2021) - [A Deep Dive into Dataset Imbalance and Bias in Face Identification](https://mlsanity.com/paper/2203.08235/) (2022) ## Built on by (most cited first, 10 of 10) - [Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data](https://mlsanity.com/paper/2412.01383/) (2024) - [SDFD: Building a Versatile Synthetic Face Image Dataset with Diverse Attributes](https://mlsanity.com/paper/2404.17255/) (2024) - [Type2Branch: Keystroke Biometrics based on a Dual-branch Architecture with Attention Mechanisms and Set2set Loss](https://mlsanity.com/paper/2405.01088/) (2024) - [FakeIDet: Exploring Patches for Privacy-Preserving Fake ID Detection](https://mlsanity.com/paper/2504.07761/) (2025) - [Towards Inclusive Face Recognition Through Synthetic Ethnicity Alteration](https://mlsanity.com/paper/2405.01273/) (2024) - [Investigation of Accuracy and Bias in Face Recognition Trained with Synthetic Data](https://mlsanity.com/paper/2507.20782/) (2025) - [Designing and Generating Diverse, Equitable Face Image Datasets for Face Verification Tasks](https://mlsanity.com/paper/2511.17393/) (2025) - [A Comparative Study on Synthetic Facial Data Generation Techniques for Face Recognition](https://mlsanity.com/paper/2512.05928/) (2025) - [Exploring Deep Learning and Ultra-Widefield Imaging for Diabetic Retinopathy and Macular Edema](https://mlsanity.com/paper/2603.08235/) (2026) - [Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation](https://mlsanity.com/paper/2608.25862/) (2026) ## Abstract (arXiv) This study investigates the possibility of mitigating the demographic biases that affect face recognition technologies through the use of synthetic data. Demographic biases have the potential to impact individuals from specific demographic groups, and can be identified by observing disparate performance of face recognition systems across demographic groups. They primarily arise from the unequal representations of demographic groups in the training data. In recent times, synthetic data have emerged as a solution to some problems that affect face recognition systems. In particular, during the generation process it is possible to specify the desired demographic and facial attributes of images, in order to control the demographic distribution of the synthesized dataset, and fairly represent the different demographic groups. We propose to fine-tune with synthetic data existing face recognition systems that present some demographic biases. We use synthetic datasets generated with GANDiffFace, a novel framework able to synthesize datasets for face recognition with controllable demographic distribution and realistic intra-class variations. We consider multiple datasets representing different demographic groups for training and evaluation. Also, we fine-tune different face recognition systems, and evaluate their demographic fairness with different metrics. Our results support the proposed approach and the use of synthetic data to mitigate demographic biases in face recognition.